Generally, it is often desired to scale a video image to an arbitrary size. It is common practice when scaling to do so separately in the horizontal and vertical directions for the video image. However, the video image using such an approach may suffer from poor image quality, including jagged edges, commonly termed as “stairstepping”.
Conventionally, a method of directional scaling interpolation is utilized to improve the video image quality by reducing stairstepping when scaling the video image. Basically, while the directional scaling interpolation is performed for the video image, the video image has a plurality of input pixels and each of the input pixels corresponds a two-dimensional window, i.e. the window size of 6 by 8 (6×8), when one of the input pixels is termed as a reference pixel. When reconstructing an enlarged video image from an original video image, it is necessary to interpolate the original input pixels therebetween for generating upsampled pixels between consecutive lines and to interpolate the original input pixels for “inserting” new pixels into the enlarged video image.
Algorithms incorporating directional interpolation techniques analyze the local gradient characteristics of the input pixels in the video image. The implement interpolation is implemented based on those local gradient characteristics along both one direction, i.e. a lowest frequency direction or a direction to the minimum local gradient level, and the other direction, i.e. a horizontal direction or a direction which is orthogonal to the lowest frequency direction, of the video image.
Moreover, the video image is composed of three-dimensional directions, including the vertical, horizontal and temporal (i.e. time-based) directions when the video image is played sequentially. However, the noises which are inherent in the temporal direction incorrectly influence the calculation of the local gradient characteristics along the lowest frequency direction and thus the determination of the other direction orthogonal to the lowest frequency direction is erroneous. Therefore, the pixel with the noises flicks in video image along at least two different temporal directions. For example, regarding to the same image region of the video image, the lowest frequency direction is determined by angle “A” at temporal direction “t”, and however, the lowest frequency direction is determined by angle “B” at temporal direction “t+1”, wherein the angle “B” is not equal to angle “A” disadvantageously, commonly termed as “flick” or “sparkle”. While playing the video image, hence the input pixels sparkle on the same plane since the inequality in angle “B” and angle “A” results in the deviation of the lowest frequency direction.
In addition, it is common practice when making a sharpness procedure separately in the horizontal and vertical directions for the scaled input pixels. However, the scaled input pixels using such a sharpness approach may suffer from poor image quality, including jagged edges. In other words, the effect of the jagged edges on the scaled input pixels cannot be completely avoided because it is inherent in the pixels along the lowest frequency direction of the scaled input pixels when employing the conventional sharpness technique. Consequently, there is a need to develop a novel image processing system for solving the aforementioned problems.